Vision-based Localization and Robot-centric Mapping in Riverine Environments

This paper presents a vision-based localization and mapping algorithm developed for an unmanned aerial vehicle (UAV) that can operate in a riverine environment. Our algorithm estimates the three-dimensional positions of point features along a river and the pose of the UAV. By detecting features surrounding a river and the corresponding reflections on the water's surface, we can exploit multiple-view geometry to enhance the observability of the estimation system. We use a robot-centric mapping framework to further improve the observability of the estimation system while reducing the computational burden. We analyze the performance of the proposed algorithm with numerical simulations and demonstrate its effectiveness through experiments with data from Crystal Lake Park in Urbana, Illinois. We also draw a comparison to existing approaches. Our experimental platform is equipped with a lightweight monocular camera, an inertial measurement unit, a magnetometer, an altimeter, and an onboard computer. To our knowledge, this is the first result that exploits the reflections of features in a riverine environment for localization and mapping